Predicting Future Performance in an ITS system via Gradient Boosting Classification

Breya Walker, University of Memphis, Memphis, TN, United States

Anne lippert, University of Memphis, Memphis, TN

Raven Davis, University of Memphis, Memphis, TN

Zhiqiang Cai, The University of Memphis, Memphis, TN

Cheng Qinyu, The University of Memphis, Memphis, TN

Genghu Shi, The University of Memphis, Memphis, TN

Arthur Graesser, The University of Memphis

Abstract

Gradient Boosting Classification (GBC) models are well known to
machine learning and artificial intelligence. Having the ability to predict user
performance is imperative to the outcomes and purpose of an intelligent tutoring
system. The Center for the Study of Adult Literacy (CSAL) intelligent tutoring
system aims to improve reading comprehension in low-literacy adult learners. A
GBC was applied to preliminary data gathered from high-literacy adult readers (N
=1800 observations). Our model was shown high accuracy in predicting users’
correct/incorrect responses to our multiple choice items. Specifically,
users’ reaction times and order of question presentation are important
features of the model to consider. Less important features are difficulty of the
item and the users reading ability. Our next steps are to apply GBC to
high-literacy college students, followed by low-literacy readers, as a test set.
Our eventual goal is to predict correctness prior to scoring.